Visualizing deaths in Finland

# Main libraries, used in each section
library(tidyverse)
library(patchwork)
library(pxweb) # for pxweb_get()

# Supporting libraries
# When these are used I have included the package name,
# e.g., geofi::get_municipalities()
library(geofi) # for get_municipalities()
library(scales) # for label_comma()
library(data.table) # for fread()
library(gridExtra) # for tableGrob()
library(paletteer) # for scale_fill_paletteer_d()

# 12at -- Vital statistics and population, 1749-2021
# https://statfin.stat.fi/PxWeb/pxweb/en/StatFin/StatFin__kuol/statfin_kuol_pxt_12at.px/
px_12at <- pxweb_get(url = 
"https://statfin.stat.fi:443/PxWeb/api/v1/en/StatFin/kuol/statfin_kuol_pxt_12at.px",
query = list("Tiedot"=c("vm01", "vm11", "vaesto"), "Vuosi"=c("*")))

df_12at <- as_tibble(as.data.frame(px_12at, column.name.type = "text", variable.value.type = "text"))

# Calculate death rate
df_12at |>
  rename(Live_births = "Live births") |>
  mutate(Year = as.integer(Year),
         Death_rate = Deaths / Population * 100000
         ) -> df_12at 

# Source: http://www.saunalahti.fi/arnoldus/kuolovuo.html
labels = tribble(
  ~Year, ~Deaths, ~Label,
  1867,  137720, "Finnish famine (1866–1868)",
  1917,  92102,  "Civil War\n(1918)",
  1948,  63846,  "Winter,\nContinuation &\nLapland Wars\n(1939–45)",
  1835,  55038,  "Smallpox,\ndysentery\n& influenza\n(1833)",
  1805,  51942,  "Finnish War\n(1808-09)",
  2008,  48659,  "In 2021 there were\n57,659 deaths in\nFinland, highest\nsince 1940s"
)

# Plot deaths of 1749 - 2021
ggplot(df_12at, aes(Year, Deaths)) +
  geom_line(size = 1.1, color = "#505085") +
  scale_x_continuous(breaks = seq(1750, 2020, 25)) +
  scale_y_continuous(labels = scales::label_comma(), breaks = seq(0, 150000, 10000)) +
  geom_text(aes(label=Label), size = 4.5, vjust = -0.5, data=labels) +
  labs(subtitle = "Deaths",
       y = NULL,
       x = NULL) +
  theme_minimal() -> yearly_deaths_plot

# Function to create the three smaller plots
create_graph <- function(df, y_stat, subtitle) {
  ggplot(df, aes(Year, {{y_stat}})) +
    geom_line(color = "#505085") +
    scale_x_continuous(breaks = seq(1750, 2020, 50)) +
    scale_y_continuous(labels = scales::label_comma()) +
    labs(subtitle = subtitle,
         y = NULL,
         x = NULL) +
    theme_minimal() +
    theme(text = element_text(size = 11))
}

# Create the smaller plots
create_graph(df_12at, Population, "Population") -> yearly_population_plot
create_graph(df_12at, Live_births, "Live births") -> yearly_births_plot
create_graph(df_12at, Death_rate, "Deaths / 100,000 people") -> yearly_death_rate_plot

# Set the layout for plots
layout <- "
AAA
AAA
BCD
"

# Combine and annotate the plots
(yearly_deaths_plot + yearly_population_plot +
                  yearly_births_plot + yearly_death_rate_plot +
                  plot_layout(design = layout)) +
plot_annotation(title = "Yearly deaths, population and births in Finland, 1749 - 2021",
caption = "source: Tilastokeskus 12at -- Vital statistics and population, 1749-2021"
) -> plot_yearly

# Save as picture
ggsave("images//1_plot_yearly.png", plot_yearly, device = "png", dpi = 96,
       width = 9, height = 9, units = c("in"))

# Get the correspondence table between municipalities and regions in 2020
# https://www.stat.fi/en/luokitukset/corrmaps/kunta_1_20200101%23maakunta_1_20200101/
region_keys_2020 <- data.table::fread("kunta_1_20200101%23maakunta_1_20200101.csv", encoding = "Latin-1",
                          col.names = c("id_1", "Municipality", "id_2", "Region"), header = F)

# 11rf -- Population according to age (1-year) and sex by area and the  # regional division of each statistical reference year, 2003-2021
# https://statfin.stat.fi/PxWeb/pxweb/en/StatFin/StatFin__vaerak/statfin_vaerak_pxt_11rf.px/
px_11rf <- pxweb_get(url = 
                       "https://statfin.stat.fi:443/PxWeb/api/v1/en/StatFin/vaerak/statfin_vaerak_pxt_11rf.px",
                     query = list(
                       "Kunta" = c("*"),
                       "Sukupuoli" = c("1", "2"),
                       "Vuosi" = c("2016", "2017", "2018", "2019", "2020"),
                       "Tiedot" = c("*"),
                       "Ikä" = c("*")))

df_11rf <- as_tibble(as.data.frame(px_11rf, column.name.type = "text", variable.value.type = "text"))

# Filter out totals, create age brackets, aggregate municipalities to regions 
df_11rf |>
  drop_na() |>
  filter(Municipality != "WHOLE COUNTRY",
         Age != "Total") |>
  left_join(region_keys_2020, by = "Municipality") |>
  mutate(Age = ifelse(Age == "100 -", "100", Age),
         Age = as.integer(Age)) |>
  mutate(Age_group = case_when(Age == 0 ~ "0",
                               between(Age, 1, 4) ~ "1 - 4",
                               between(Age, 5, 9) ~ "5 - 9",
                               between(Age, 10, 14) ~ "10 - 14",
                               between(Age, 15, 19) ~ "15 - 19",
                               between(Age, 20, 24) ~ "20 - 24",
                               between(Age, 25, 29) ~ "25 - 29",
                               between(Age, 30, 34) ~ "30 - 34",
                               between(Age, 35, 39) ~ "35 - 39",
                               between(Age, 40, 44) ~ "40 - 44",
                               between(Age, 45, 49) ~ "45 - 49",
                               between(Age, 50, 54) ~ "50 - 54",
                               between(Age, 55, 59) ~ "55 - 59",
                               between(Age, 60, 64) ~ "60 - 64",
                               between(Age, 65, 69) ~ "65 - 69",
                               between(Age, 70, 74) ~ "70 - 74",
                               between(Age, 75, 79) ~ "75 - 79",
                               between(Age, 80, 84) ~ "80 - 84",
                               between(Age, 85, 89) ~ "85 - 89",
                               between(Age, 90, 94) ~ "90 - 94",
                               between(Age, 95, 150) ~ "95 -",)) |>
  rename(Population = "Population 31 Dec") |>
  select(Municipality, Year, Sex, Population, Region, Age_group) |>
  rename(Age = Age_group) |>
  mutate(Region = case_when(Municipality == "Juankoski" ~ "North Savo",
                            Municipality == "Luvia" ~ "Satakunta",
                            Municipality == "Valtimo" ~ "North Karelia",
                            TRUE ~ Region)) |>
  group_by(Region, Year, Age, Sex) |>
  summarise(Population = sum(Population)) -> df_11rf

# Create a df with all regions aggregated to one Finnish population per age, sex and year
df_11rf |>
  group_by(Year, Age, Sex) |>
  summarise(Population_All_Regions = sum(Population)) -> df_all_regions

# Add column Population_All_Regions to the main df
df_11rf |>
  left_join(df_all_regions, by = c("Year", "Age", "Sex")) -> df_11rf

# Get Deaths by Underlying cause of death (time series classification), Age, Information, Gender and Year
# https://statfin.stat.fi/PxWeb/pxweb/en/StatFin/StatFin__ksyyt/statfin_ksyyt_pxt_11bs.px/
px_11bs <- pxweb_get(url = 
                       "https://statfin.stat.fi:443/PxWeb/api/v1/en/StatFin/ksyyt/statfin_ksyyt_pxt_11bs.px",
                     query = list(
                       "Tilaston peruskuolemansyy (aikasarjaluokitus)" = c("*"),
                       "Sukupuoli" = c("1", "2"),
                       "Vuosi" = c("2016", "2017", "2018", "2019", "2020"),
                       "Ikä" = c("*"),
                       "Tiedot" = c("*")))

df_11bs <- as_tibble(as.data.frame(px_11bs, column.name.type = "text", variable.value.type = "text"))

# Save all the unique causes as an atomic vector for later use
df_11bs$`Underlying cause of death (time series classification)` |> unique() -> causes

# Rename columns and filter out totals
df_11bs |>
  rename(Cause = "Underlying cause of death (time series classification)",
         Sex = Gender) |>
  filter(Age != "Total") -> df_11bs

# Add Cause and Deaths columns to the main df to calculate expected deaths, aggregate years together
df_11rf |>
  left_join(df_11bs, by = c("Age", "Sex", "Year")) |>
  rename(Deaths_All_Regions = Deaths) |>
  mutate(Expected = (Population / Population_All_Regions) * Deaths_All_Regions) |>
  group_by(Region, Cause) |>
  summarise(Expected = round(sum(Expected), 2)) -> df_11rf

# At this point df_11rf contains expected deaths by cause and region. Next we need to know the actual deaths.

# For causes of death per region
# https://statfin.stat.fi/PxWeb/pxweb/en/StatFin/StatFin__ksyyt/statfin_ksyyt_pxt_11bt.px/
px_11bt <- pxweb_get(url = 
                       "https://statfin.stat.fi:443/PxWeb/api/v1/en/StatFin/ksyyt/statfin_ksyyt_pxt_11bt.px",
                     query = list(
                       "Maakunta" = c("MK01", "MK02", "MK04", "MK05", "MK06", "MK07", "MK08", "MK09", "MK10", 
                                      "MK11", "MK12", "MK13", "MK14", "MK15", "MK16", "MK17", "MK18", "MK19", "MK21"),
                       "Tilaston peruskuolemansyy (aikasarjaluokitus)" = c("*"),
                       "Vuosi" = c("2016", "2017", "2018", "2019", "2020"),
                       "Tiedot" = c("*")))

df_11bt <- as_tibble(as.data.frame(px_11bt, column.name.type = "text", variable.value.type = "text"))

# For Finnish translations
# https://statfin.stat.fi/PxWeb/pxweb/fi/StatFin/StatFin__ksyyt/statfin_ksyyt_pxt_11bt.px/
px_11bt_fi <- pxweb_get(url = 
                          "https://pxdata.stat.fi:443/PxWeb/api/v1/fi/StatFin/ksyyt/statfin_ksyyt_pxt_11bt.px",
                        query = list(
                          "Maakunta" = c("MK01", "MK02", "MK04", "MK05", "MK06", "MK07", "MK08", "MK09", "MK10", 
                                         "MK11", "MK12", "MK13", "MK14", "MK15", "MK16", "MK17", "MK18", "MK19", "MK21"),
                          "Tilaston peruskuolemansyy (aikasarjaluokitus)" = c("*"),
                          "Vuosi" = c("2020"),
                          "Tiedot" = c("*")))

df_11bt_fi <- as_tibble(as.data.frame(px_11bt_fi, column.name.type = "text", variable.value.type = "text"))

# Finnish translations need to be joined to the English df. For that we 
# create ID columns for region and cause.
df_11bt_fi |>
  rename(Cause_fi = "Tilaston peruskuolemansyy (aikasarjaluokitus)") |>
  separate(Maakunta, into = c("ID", "Region_fi"), sep = " ", extra = "merge") |>
  mutate(Cause_id = str_match(Cause_fi, "[:graph:]+[:space:]")) |>
  select(ID, Region_fi, Cause_fi, Cause_id) -> df_11bt_fi

# Also prepare the English version for future joining similarly
df_11bt |>
  rename(Cause = "Underlying cause of death (time series classification)") |>
  mutate(Year = as.integer(Year)) |>
  separate(Region, into = c("ID", "Region"), sep = " ", extra = "merge") |>
  group_by(ID, Region, Cause) |>
  summarise(Deaths = sum(Deaths)) |>
  mutate(Cause_id = str_match(Cause, "[:graph:]+[:space:]")) -> df_11bt

# Join the Finnish translations for region and cause
df_11bt |>
  left_join(df_11bt_fi, by = c("ID", "Cause_id")) -> df_11bt

# Join main df (expected deaths) with actual deaths. Now we can calculate SMRs.
df_11rf |>
  left_join(df_11bt, by = c("Region", "Cause")) |>
  mutate(SMR = round(Deaths / Expected, 2)) |> # Standardized mortality ratio
  select(Region_fi, Region,  Cause_fi, Cause, Deaths, Expected, SMR
  ) -> df_11rf

# Get map so we can draw the regions
df_region_map <- geofi::get_municipalities(year = 2020, scale = 4500)

# Grouping by regions, because we don't need individual municipalities or most other columns
df_region_map |>
  group_by(maakunta_name_en) |>
  summarise() |>
  rename(Region = maakunta_name_en) -> df_region_map

# Add main df to the map df
df_region_map |>
  left_join(df_11rf, by = "Region") -> df_cause_region

# Function: Creates a plot and a table for the given cause of death
create_plot <- function(cause_en){
  
  # Filter by the given cause
  df_cause_region |>
    filter(Cause == cause_en) -> df
  
  # Combine the English and Finnish names for the title
  title_1 <- paste0(df[["Cause"]][[1]], "\n",
                    df[["Cause_fi"]][[1]])
  
  # Remove the unnecessary things (those in parentheses) from the title
  title_1 <- str_remove_all(title_1, "\\(.+\\)") 
  
  # Create the plot
  ggplot(df) + 
    geom_sf(aes(geometry = geom, fill = SMR)) +
    scale_fill_gradient2(low = muted("blue"),
                         mid = "white",
                         high = muted("red"),
                         midpoint = 1,
                         space="Lab") + 
    labs(fill = NULL) +
    theme(legend.position = c(0.2, 0.65),
          legend.background = element_blank(),
          legend.key.size = unit(0.2, "in"),
          legend.text = element_text(size = 9),
          plot.title = element_text(size = 11),
          plot.subtitle = element_text(size = 10),
          plot.title.position = "plot") -> p
  
  # Set Region_fi column as row names and select only few columns for the table
  df |>
    ungroup() |>
    filter(Cause == cause_en) |>
    column_to_rownames(var = "Region_fi") |>
    arrange(desc(SMR)) |>
    select(Deaths, Expected, SMR) -> df  
  
  # Create the table and combine it with the plot
  p <- p + gridExtra::tableGrob(df, theme = ttheme_default(base_size = 9)) +
    plot_annotation(title = title_1,
                    theme = theme(plot.title = element_text(size = 14)))
}

# Save plots for all causes as pictures. Warning: creates 65 pictures!
for (i in seq(from = 1, to = length(causes))) {
  p <- create_plot(causes[i])
  name = paste0("images//region//", as.character(i), ".png")
  ggsave(filename = name, p, device = "png", dpi = 96,
         width = 7.3, height = 6, units = c("in"))
}

# 11bs -- Deaths by underlying cause of death (time series classification), age and gender, 1969-2020
# https://statfin.stat.fi/PxWeb/pxweb/en/StatFin/StatFin__ksyyt/statfin_ksyyt_pxt_11bs.px/
px_11bs <- pxweb_get(url = 
                       "https://statfin.stat.fi:443/PxWeb/api/v1/en/StatFin/ksyyt/statfin_ksyyt_pxt_11bs.px",
                     query = list("Sukupuoli" = c("SSS"), "Vuosi" = c("*"), "Tiedot" = c("*"),
                                  "Ikä" = c("SSS"), "Tilaston peruskuolemansyy (aikasarjaluokitus)" = c("*")))
df_11bs <- as_tibble(as.data.frame(px_11bs, column.name.type = "text", variable.value.type = "text"))

df_11bs |>
  rename(Cause = "Underlying cause of death (time series classification)") |>
  mutate(Year = as.integer(Year)) |>
  filter(str_detect(Cause, "^[:digit:][:digit:][:space:]"))|>
  select(Cause, Year, Deaths) |>
  mutate(Cause = str_remove_all(Cause, " \\(.+\\)")) |>
  group_by(Cause) |>
  mutate(Sum_deaths = sum(Deaths)) |>
  filter(Sum_deaths >= 1000,
         Cause != "54 No death certificate") |>
  mutate(Percentage = Deaths/sum(Deaths),
         Weight = Year-1994.5,
         Weighted_distance = Percentage * Weight,
         Sum_weighted_distance = sum(Weighted_distance)) |>
  ungroup() -> df_11bs

create_trend_graph <- function(dt, legend_position, subtitle_1){
  ggplot(dt, aes(Year, Deaths, color = Cause)) +
    geom_line(size = 1.5) +
    theme_bw() +
    labs(subtitle = subtitle_1,
         x = NULL, y = NULL) +
    theme(plot.background = element_rect(fill = "#FCFCFC",
                                         colour = "#FCFCFC"),
          legend.position = legend_position,
          legend.background = element_blank(),
          legend.title = element_blank())
}

df_11bs |>
  slice_min(order_by = Sum_weighted_distance, n = 52*8) |>
  mutate(Cause = as_factor(Cause),
         Cause = fct_reorder2(Cause, desc(Year), Deaths)) |>
  create_trend_graph(c(0.84, 0.73), "Causes getting less common") -> min_plot

df_11bs |>
  slice_max(order_by = Sum_weighted_distance, n = 52*8) |>
  mutate(Cause = as_factor(Cause),
         Cause = fct_reorder2(Cause, Year, Deaths)) |>
  create_trend_graph(c(0.35, 0.69), "Causes getting more common") -> max_plot

(min_plot / max_plot) +
  plot_annotation(
    theme = theme(plot.background = element_rect(fill = "#FCFCFC", 
                                                 colour = "#FCFCFC")),
    title = "Trends (deaths by year, 1969 - 2020)",
    caption = "source: Tilastokeskus 11bs -- Deaths by underlying cause of death, 1969-2020"
  ) -> plot_min_max
  
# Save as picture
ggsave("images//plot_min_max.png", plot_min_max, device = "png", dpi = 96,
       width = 9, height = 9, units = c("in"))

# 12ah -- Deaths by month, 1945-2021
# https://statfin.stat.fi/PxWeb/pxweb/en/StatFin/StatFin__kuol/statfin_kuol_pxt_12ah.px/
px_12ah <- pxweb_get(url = 
"https://statfin.stat.fi:443/PxWeb/api/v1/en/StatFin/kuol/statfin_kuol_pxt_12ah.px",
 query = list("Tapahtumakuukausi" = c("M01", "M02", "M03", "M04", "M05", "M06",
                                      "M07", "M08", "M09", "M10", "M11", "M12"),
              "Tiedot"=c("*"), "Vuosi"=c("*")))

df_12ah <- as_tibble(as.data.frame(px_12ah, column.name.type = "text", variable.value.type = "text"))

# Vector of average days / month to calculate daily stats
avg_days_per_month = c(31, 28.25, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31)

# Add daily deaths, short name for month and decade columns
df_12ah |>
  rename(Month = "Month of occurrence") |>
  mutate(Year = as.integer(Year),
         Month = as_factor(Month),
         Deaths_daily = as.integer(Deaths / avg_days_per_month),
         Month_short = as_factor(rep(month.abb, n()/12)),
         Decade = as.character(paste0(Year - Year %% 10, "s")),
         Decade = str_replace_all(Decade, "1940s", "1945-1949")
         ) -> df_12ah

# Calculate mean of daily deaths by month for 1945-2021 and create a plot
df_12ah |>
  group_by(Month) |>
  summarise(Deaths_daily = round(mean(Deaths_daily))) |>
  ggplot(aes(Month, Deaths_daily)) +
  geom_col(fill = "#393b15") +
  geom_text(aes(label = Deaths_daily), vjust = 1.5,
            color = "white", size = 4) +
  theme_bw() +
  labs(y = NULL,
       x = NULL) -> daily_deaths_month_plot

# Create the daily deaths by month by decade plots
df_12ah |>
  filter(Decade != "2020s") |>
  group_by(Decade, Month_short) |>
  summarise(Deaths_daily = round(mean(Deaths_daily))) |>
  ggplot(aes(Month_short, Deaths_daily)) +
  geom_col(fill = "#393b15") +
  theme_bw() +
  facet_wrap(vars(Decade), nrow = 2) +
  geom_text(aes(label = Deaths_daily),
            color = "white", size = 3.2, angle = 270, hjust = -0.1) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.15)) +
  labs(subtitle = "By decade",
       y = NULL,
       x = NULL,
       caption = "source: Tilastokeskus 12ah -- Deaths by month, 1945-2021"
       ) -> daily_deaths_decade_plot

# Combine the created plots and add annotations
plot_months <- daily_deaths_month_plot / daily_deaths_decade_plot +
  plot_annotation(title = "Daily deaths in Finland by month (1945-2021)",
                  subtitle = "Death rates are higher in winter months")

# Save as a picture
ggsave("images//2_plot_months.png", plot_months, device = "png", dpi = 96,
       width = 9, height = 9, units = c("in"))

# Notice: To create the second plots (region) in this section we are
# reusing df_region_map (created in the section Region and cause (2016 - 2020))

# 12am -- Life expectancy at birth by sex, 1751-2021
# https://statfin.stat.fi/PxWeb/pxweb/en/StatFin/StatFin__kuol/statfin_kuol_pxt_12am.px/
px_12am <- pxweb_get(url = 
"https://statfin.stat.fi:443/PxWeb/api/v1/en/StatFin/kuol/statfin_kuol_pxt_12am.px",
query = list( "Sukupuoli" = c("1", "2"), "Vuosi" = c("*"), "Tiedot" = c("*")))

df_12am <- as_tibble(as.data.frame(px_12am, column.name.type = "text", variable.value.type = "text"))

# Some years are in the form of "1751-1760", so mutate them to integers
df_12am |>
  rename(Life_exp = "Life expectancy at birth, years") |>
  mutate(Year = str_sub(Year, 1, 4)) |>
  mutate(Year = as.integer(Year),
         Sex = as_factor(Sex)) -> df_12am

# Create the first of four plots
ggplot(df_12am, aes(Year, Life_exp, color = Sex)) +
  geom_line(size = 1.1) +
  scale_colour_hue(direction = -1) +
  theme(legend.position = c(0.25, 0.75),
        legend.background = element_blank(),
        plot.title = element_text(hjust = 0.06),
        plot.tag.position = c(0.2, 1)) +
  labs(subtitle = "Life expectancy by year (1751-2021)", y = "age", x = "year"
       ) -> life_plot_1

# 12ap -- Life table by age and sex, 1986-2020
# https://statfin.stat.fi/PxWeb/pxweb/en/StatFin/StatFin__kuol/statfin_kuol_pxt_12ap.px/
# This is used to to create the next three plots
px_12ap <- pxweb_get(url = 
"https://statfin.stat.fi:443/PxWeb/api/v1/en/StatFin/kuol/statfin_kuol_pxt_12ap.px",
query = list("Sukupuoli" = c("1", "2"), "Vuosi" = c("*"), "Tiedot" = c("*"),
             "Ikä" = c("*")))

df_12ap <- as_tibble(as.data.frame(px_12ap, column.name.type = "text", variable.value.type = "text"))

# Calculate yearly means from 1986-2020 data   
df_12ap |>
  mutate(Age = as.integer(Age),
         Year = as.integer(Year),
         Sex = as_factor(Sex)) |>
  rename(Life_exp = "Life expectancy, years",
         Survivors = "Survivors of 100,000 born alive",
         Death_prob = "Probability of death, per mille") |>
  group_by(Sex, Age) |>
  summarise(Life_exp = mean(Life_exp),
            Survivors = mean(Survivors),
            Death_prob = mean(Death_prob)) |>
  mutate(Death_prob = Death_prob / 10 # Changing to percent 
         ) -> df_12ap

# Function used to create the remaining three plots
create_plot <- function(ycol, ylab, subtitle_1) {
  ggplot(df_12ap, aes(Age, .data[[ycol]], color = Sex)) +
    geom_line(size = 1.1, na.rm = T) +
    scale_colour_hue(direction = -1) +
    scale_y_continuous(labels = scales::label_comma()) +
    theme(legend.position = "none") +
    labs(subtitle = subtitle_1, y = ylab, x = "age")
}

# Create the plots 2-4
create_plot("Life_exp", "years", "Remaining life expectancy (1986-2020)") -> life_plot_2
create_plot("Survivors", "survivors", "Survival of 100,000 born alive (1986-2020)") -> life_plot_3
create_plot("Death_prob", "percent", "Yearly probability of death (1986-2020)") -> life_plot_4

# Combine and annotate plots 1-4
plot_life_exp_1 <- (life_plot_1 / life_plot_2 | life_plot_3 / life_plot_4) +
  plot_annotation(title = "Longetivity by sex",
                  caption = "sources: Tilastokeskus 12am (1751-2021) & 12ap (1986-2020)")

# 12an -- Life expectancy at birth by sex and region
# https://statfin.stat.fi/PxWeb/pxweb/en/StatFin/StatFin__kuol/statfin_kuol_pxt_12an.px/
px_12an <- pxweb_get(url = 
                       "https://statfin.stat.fi:443/PxWeb/api/v1/en/StatFin/kuol/statfin_kuol_pxt_12an.px",
                     query = list(
                       "Maakunta" = c("MK01", "MK02", "MK04", "MK05", "MK06", "MK07", "MK08", "MK09", "MK10", 
                                      "MK11", "MK12", "MK13", "MK14", "MK15", "MK16", "MK17", "MK18", "MK19", "MK21"),
                       "Vuosi" = c("2020"),
                       "Sukupuoli" = c("1", "2"),
                       "Tiedot" = c("*")))

df_12an <- as_tibble(as.data.frame(px_12an, column.name.type = "text", variable.value.type = "text"))

# Remove ID from region name
df_12an |> 
  rename(Life_exp = "Life expectancy at birth, years") |>
  mutate(Region = str_remove_all(Region, "MK.. ")) -> df_12an

# Add map. Notice: df_region_map was created in the section Region and cause (2016 - 2020)
df_region_map |>
  left_join(df_12an, by = c("Region")) -> df_life_region

# Create Females plot
df_life_region |>
  filter(Sex == "Females") |>
  ggplot() + 
  geom_sf(aes(geometry = geom, fill = Life_exp)) +
  scale_fill_distiller(palette = "Spectral", direction = 1, n.breaks = 9) +
  labs(subtitle = "Females", fill = NULL) +
  theme(legend.position = c(0.15, 0.63),
        legend.background = element_blank(),
        legend.key.size = unit(0.4, "in"),
        legend.text = element_text(size = 9),
        panel.background = element_rect(colour = "#FF0000FF"),
        plot.margin = margin(r = 1, unit = "cm"),
        plot.subtitle = element_text(size = 13)
  ) -> life_exp_region_f_plot

# Create Males plot
df_life_region |>
  filter(Sex == "Males") |>
  ggplot() + 
  geom_sf(aes(geometry = geom, fill = Life_exp)) +
  scale_fill_distiller(palette = "Spectral", direction = 1, n.breaks = 9) +
  labs(subtitle = "Males", fill = NULL) +
  theme(legend.position = c(0.15, 0.63),
        legend.background = element_blank(),
        legend.key.size = unit(0.4, "in"),
        legend.text = element_text(size = 9),
        panel.background = element_rect(colour = "#00FFFFFF"),
        plot.subtitle = element_text(size = 13)
  ) -> life_exp_region_m_plot

# Combine and annotate region plots
plot_life_exp_2 <- life_exp_region_f_plot + life_exp_region_m_plot +
  plot_annotation(title = "Life expectancy by region (2018-2020)",
    caption = "source: Tilastokeskus 12an -- Life expectancy at birth by sex and region")

# Save images
ggsave("images//3_plot_life_exp_1.png", plot_life_exp_1, device = "png", dpi = 96,
       width = 9, height = 7, units = c("in"))
ggsave("images//3_plot_life_exp_2.png", plot_life_exp_2, device = "png", dpi = 96,
       width = 7, height = 6.3, units = c("in"))

# 11by -- Suicides by age and gender, 1921-2020
# https://statfin.stat.fi/PxWeb/pxweb/en/StatFin/StatFin__ksyyt/statfin_ksyyt_pxt_11by.px/
url = "https://statfin.stat.fi:443/PxWeb/api/v1/en/StatFin/ksyyt/statfin_ksyyt_pxt_11by.px"
query = list("Sukupuoli" = c("1", "2"), "Tiedot"=c("*"), "Vuosi"=c("*"), "Ikä" = c("*"))
px_11by <- pxweb_get(url = url, query = query)
df_11by <- as_tibble(as.data.frame(px_11by, column.name.type = "text", variable.value.type = "text"))

# Create the yearly plot
df_11by |>
  filter(Age == "Total") |>
  mutate(Year = as.integer(Year),
         Sex = as_factor(Gender)) |>
  ggplot(aes(Year, Suicides, color = Sex)) +
  geom_line(size = 1.1) +
  scale_colour_hue(direction = -1) +
  theme(legend.position = c(0.08, 0.9),
        legend.background = element_blank(),
        legend.title = element_blank()) +
  labs(subtitle = "By year",
       y = NULL,
       x = NULL) -> s_plot_1

# Create the age group plot
df_11by |>
  filter(Age != "Total") |>
  group_by(Age, Gender) |>
  summarise(Suicides = sum(Suicides)) |>
  mutate(Age = as_factor(Age),
         Sex = as_factor(Gender)) |>
  ggplot(aes(Sex, Suicides, fill = Age)) +
  geom_col(position = "dodge") +
  scale_y_continuous(expand = expansion(mult = c(0))) +
  paletteer::scale_fill_paletteer_d("ggthemes::manyeys") +
  guides(fill = guide_legend(ncol = 2, byrow = F)) +
  theme(legend.position = c(0.12, 0.68),
        legend.background = element_blank(),
        legend.title = element_blank()) +
  labs(subtitle = "By age group",
       y = NULL,
       x = NULL) -> s_plot_2

# Combine and annotate
plot_s_1 <- (s_plot_1 / s_plot_2) +
  plot_annotation(title = "Suicides (1921-2020)",
                  caption = "source: Tilastokeskus 11by -- Suicides by age and gender, 1921-2020")

# Save image
ggsave("images//4_plot_s_1.png", plot_s_1, device = "png", dpi = 96,
       width = 9, height = 8, units = c("in"))
  
# 12ef -- Accidental and violent deaths by underlying cause of death, 1998-2020
# https://statfin.stat.fi/PxWeb/pxweb/en/StatFin/StatFin__ksyyt/statfin_ksyyt_pxt_12ef.px/
url = "https://statfin.stat.fi:443/PxWeb/api/v1/en/StatFin/ksyyt/statfin_ksyyt_pxt_12ef.px"
query = list("Sukupuoli" = c("1", "2"), "Tiedot"=c("ksyylkm3"), "Vuosi"=c("*"),
             "Ikä" = c("SSS"), "Tapaturmat ja väkivalta (ulkoisten syiden luokitus)" =
               c("073", "074", "075", "076", "077", "078", "079", "080", "081",
                 "082", "083", "084", "085", "086", "087", "088", "089", "090",
                 "091", "092", "093", "094", "095", "096", "097"))
px_12ef <- pxweb_get(url = url, query = query)
df_12ef <- as_tibble(as.data.frame(px_12ef, column.name.type = "text", variable.value.type = "text"))

# Combine years and shorten the names of causes
df_12ef |>
  rename(Cause = "Accidents and violence (short list of external causes)",
         Deaths = "Total accidental and violent deaths",
         Sex = "Gender") |>
  group_by(Cause, Sex) |>
  summarise(Deaths = sum(Deaths)) |>
  mutate(Sex = as_factor(Sex),
         Cause = str_remove(Cause, "[:digit:]{3}[:space:]Suicide "),
         Cause = str_remove(Cause, "^by "),
         Cause = str_to_sentence(Cause),
         Cause = str_replace(Cause, "autonomic nervous system", "ANS")
  ) -> df_12ef

# Calculate factor levels for Cause (fewest total deaths to highest)
df_12ef |>
  group_by(Cause) |>
  summarise(Deaths = sum(Deaths)) |>
  filter(Deaths > 0) |>
  arrange(Deaths) |>
  pull(Cause) -> cause_levels

# Set levels for Cause, create plot
df_12ef |>
  filter(Cause %in% cause_levels) |> # Filter out those causes with 0 deaths
  mutate(Cause = factor(Cause, levels = cause_levels)) |>
  ggplot(aes(Cause, Deaths, fill = Sex)) +
  geom_col() +
  scale_x_discrete(expand = expansion(mult = c(0, 0))) +
  scale_y_continuous(expand = expansion(mult = c(0, 0))) +
  coord_flip() +
  labs(subtitle = "By method (1998 - 2020)",
       caption = "source: Tilastokeskus 12ef -- Accidental and violent deaths by underlying cause of death, 1998-2020",
       x = NULL, y = NULL) +
  theme(legend.position = c(0.91, 0.2),
        legend.background = element_blank(),
        legend.title = element_blank(),
        axis.text.y = element_text(size = 10),
        plot.title.position = "plot",
        plot.subtitle = element_text(hjust = 0.06)) -> plot_s_2

# Save picture
ggsave("images//4_plot_s_2.png", plot_s_2, device = "png", dpi = 96,
       width = 9, height = 7, units = c("in"))

# 12z6 -- Deaths by month, underlying cause of death and gender, 1971M01-2020M12
# https://statfin.stat.fi/PxWeb/pxweb/en/StatFin/StatFin__ksyyt/statfin_ksyyt_pxt_12z6.px/
url = "https://statfin.stat.fi:443/PxWeb/api/v1/en/StatFin/ksyyt/statfin_ksyyt_pxt_12z6.px"
query = list("Sukupuoli" = c("1", "2"), "Tiedot"=c("*"), "Kuukausi"=c("*"),
             "Tilaston peruskuolemansyy (aikasarjaluokitus)" = c("50"))
px_12z6_50 <- pxweb_get(url = url, query = query)
df_12z6_50 <- as_tibble(as.data.frame(px_12z6_50, column.name.type = "text", variable.value.type = "text"))

# Combine years, give months short names, calculate daily deaths 
df_12z6_50 |>
  separate(Month, into = c("Year", "Month"), sep = "M") |>
  group_by(Month, Gender) |>
  summarise(Deaths = mean(Deaths)) |>
  ungroup() |>
  mutate(Month = case_when(Month == "01" ~ "Jan",
                           Month == "02" ~ "Feb",
                           Month == "03" ~ "Mar",
                           Month == "04" ~ "Apr",
                           Month == "05" ~ "May",
                           Month == "06" ~ "Jun",
                           Month == "07" ~ "Jul",
                           Month == "08" ~ "Aug",
                           Month == "09" ~ "Sep",
                           Month == "10" ~ "Oct",
                           Month == "11" ~ "Nov",
                           Month == "12" ~ "Dec"),
         Month = as_factor(Month),
         Days_in_month = rep(c(31, 28.25, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31), each = 2),
         Deaths_daily = round(Deaths / Days_in_month, 2)) -> df_12z6_50

# Function for creating the monthly plots
create_plot <- function(gender, panel_color) {
  df_12z6_50 |>
    filter(Gender == gender) |>
    ggplot(aes(Month, Deaths_daily)) +
    geom_col(fill = "#393b15") +
    geom_text(aes(label = Deaths_daily), vjust = 1.5,
              color = "white", size = 4) +
    theme_bw() +
    labs(subtitle = gender,
         y = NULL,
         x = NULL) +
    theme(panel.border = element_rect(colour = panel_color))
}

# Create monthly plots
create_plot("Females", "#FF0000FF") -> F_plot
create_plot("Males", "#00FFFFFF") -> M_plot

# Combine and annotate
(F_plot / M_plot) +
plot_annotation(title = "Daily suicides by month (Jan 1971 - Dec 2020)",
                subtitle = "Warmer months have higher suicide rates",
                caption = "source: Tilastokeskus 2z6 -- Deaths by month, underlying cause of death and gender"
                ) -> plot_s_3

# Save as picture
ggsave("images//4_plot_s_3.png", plot_s_3, device = "png", dpi = 96,
       width = 9, height = 7, units = c("in"))